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	---
	license: llama2
	model-index:
	- name: Phind-CodeLlama-34B-v1
	results:
	- task:
	type: text-generation
	dataset:
	type: openai_humaneval
	name: HumanEval
	metrics:
	- name: pass@1
	type: pass@1
	value: 73.8%
	verified: false
	tags:
	- code llama
	- lmdeploy
	---
	This is [Phind/Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2) quantized to LMDeploy 4bit AWQ with the following config:
	```bash
	python3 -m lmdeploy.lite.apis.auto_awq \
	--model ./Phind-CodeLlama-34B-v2 \
	--w_bits 4 \
	--w_group_size 128 \
	--work_dir ./quant
	```
	# Original Model Card:

	# Phind-CodeLlama-34B-v2
	We've fine-tuned Phind-CodeLlama-34B-v1 on an additional 1.5B tokens high-quality programming-related data, achieving 73.8% pass@1 on HumanEval. It's the current state-of-the-art amongst open-source models.

	Furthermore, this model is instruction-tuned on the Alpaca/Vicuna format to be steerable and easy-to-use.

	More details can be found on our [blog post](https://www.phind.com/blog/code-llama-beats-gpt4).

	## Model Details
	This model is fine-tuned from Phind-CodeLlama-34B-v1 and achieves 73.8% pass@1 on HumanEval.

	Phind-CodeLlama-34B-v2 is multi-lingual and is proficient in Python, C/C++, TypeScript, Java, and more.

	## Dataset Details
	We fined-tuned on a proprietary dataset of 1.5B tokens of high quality programming problems and solutions. This dataset consists of instruction-answer pairs instead of code completion examples, making it structurally different from HumanEval. LoRA was not used -- both models are a native finetune. We used DeepSpeed ZeRO 3 and Flash Attention 2 to train these models in 15 hours on 32 A100-80GB GPUs. We used a sequence length of 4096 tokens.

	## How to Get Started with the Model

	Make sure to install Transformers from the main git branch:

	```bash
	pip install git+https://github.com/huggingface/transformers.git
	```

	## How to Prompt the Model
	This model accepts the Alpaca/Vicuna instruction format.

	For example:

	```
	### System Prompt
	You are an intelligent programming assistant.

	### User Message
	Implement a linked list in C++

	### Assistant
	...
	```

	## How to reproduce HumanEval Results

	To reproduce our results:

	```python

	from transformers import AutoTokenizer, LlamaForCausalLM
	from human_eval.data import write_jsonl, read_problems
	from tqdm import tqdm

	# initialize the model

	model_path = "Phind/Phind-CodeLlama-34B-v2"
	model = LlamaForCausalLM.from_pretrained(model_path, device_map="auto")
	tokenizer = AutoTokenizer.from_pretrained(model_path)

	# HumanEval helper

	def generate_one_completion(prompt: str):
	tokenizer.pad_token = tokenizer.eos_token
	inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096)

	# Generate
	generate_ids = model.generate(inputs.input_ids.to("cuda"), max_new_tokens=384, do_sample=True, top_p=0.75, top_k=40, temperature=0.1)
	completion = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	completion = completion.replace(prompt, "").split("\n\n\n")[0]

	return completion

	# perform HumanEval
	problems = read_problems()

	num_samples_per_task = 1
	samples = [
	dict(task_id=task_id, completion=generate_one_completion(problems[task_id]["prompt"]))
	for task_id in tqdm(problems)
	for _ in range(num_samples_per_task)
	]
	write_jsonl("samples.jsonl", samples)

	# run `evaluate_functional_correctness samples.jsonl` in your HumanEval code sandbox
	```

	## Bias, Risks, and Limitations

	<!-- This section is meant to convey both technical and sociotechnical limitations. -->
	This model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.


	## Training details

	<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

	- Hardware Type: 32x A100-80GB
	- Hours used: 480 GPU-hours
	- Cloud Provider: AWS
	- Compute Region: us-east-1